# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

import collections
import io
import json
import librosa
import numpy as np
import soundfile as sf
import time
import torch
from scipy.io.wavfile import read
from .text import SOS_TOK, EOS_TOK


def get_mask_from_lengths(lengths):
    max_len = torch.max(lengths).item()
    ids = torch.arange(0, max_len, out=torch.cuda.LongTensor(max_len))
    mask = (ids < lengths.unsqueeze(1))
    return mask


def load_wav_to_torch(full_path, sr=None):
    data, sr = librosa.load(full_path, sr=sr)
    data = np.clip(data, -1, 1)  # potentially out of [-1, 1] due to resampling
    data = data * 32768.0  # match values loaded by scipy
    return torch.FloatTensor(data.astype(np.float32)), sr


def read_binary_audio(bin_data, tar_sr=None):
    """
    read binary audio (`bytes` or `uint8` `numpy.ndarray`) to `float32`
    `numpy.ndarray`

    RETURNS:
        data (np.ndarray) : audio of shape (n,) or (2, n)
        tar_sr (int) : sample rate
    """
    data, ori_sr = sf.read(io.BytesIO(bin_data), dtype='float32')
    data = data.T
    if (tar_sr is not None) and (ori_sr != tar_sr):
        data = librosa.resample(data, ori_sr, tar_sr)
    else:
        tar_sr = ori_sr
    data = np.clip(data, -1, 1)
    data = data * 32768.0
    return torch.FloatTensor(data.astype(np.float32)), tar_sr


def load_filepaths_and_text(filename):
    with open(filename, encoding='utf-8') as f:
        data = [json.loads(line.rstrip()) for line in f]
    return data


def to_gpu(x):
    x = x.contiguous()

    if torch.cuda.is_available():
        x = x.cuda(non_blocking=True)
    return torch.autograd.Variable(x)


def load_code_dict(path, add_sos=False, add_eos=False):
    if not path:
        return {}

    with open(path, 'r') as f:
        codes = ['_'] + [line.rstrip() for line in f]  # '_' for pad
    code_dict = {c: i for i, c in enumerate(codes)}

    if add_sos:
        code_dict[SOS_TOK] = len(code_dict)
    if add_eos:
        code_dict[EOS_TOK] = len(code_dict)
    assert(set(code_dict.values()) == set(range(len(code_dict))))

    return code_dict


def load_obs_label_dict(path):
    if not path:
        return {}
    with open(path, 'r') as f:
        obs_labels = [line.rstrip() for line in f]
    return {c: i for i, c in enumerate(obs_labels)}


# A simple timer class inspired from `tnt.TimeMeter`
class CudaTimer:
    def __init__(self, keys):
        self.keys = keys
        self.reset()

    def start(self, key):
        s = torch.cuda.Event(enable_timing=True)
        s.record()
        self.start_events[key].append(s)
        return self

    def stop(self, key):
        e = torch.cuda.Event(enable_timing=True)
        e.record()
        self.end_events[key].append(e)
        return self

    def reset(self):
        self.start_events = collections.defaultdict(list)
        self.end_events = collections.defaultdict(list)
        self.running_times = collections.defaultdict(float)
        self.n = collections.defaultdict(int)
        return self

    def value(self):
        self._synchronize()
        return {k: self.running_times[k] / self.n[k] for k in self.keys}

    def _synchronize(self):
        torch.cuda.synchronize()
        for k in self.keys:
            starts = self.start_events[k]
            ends = self.end_events[k]
            if len(starts) == 0:
                raise ValueError("Trying to divide by zero in TimeMeter")
            if len(ends) != len(starts):
                raise ValueError("Call stop before checking value!")
            time = 0
            for start, end in zip(starts, ends):
                time += start.elapsed_time(end)
            self.running_times[k] += time * 1e-3
            self.n[k] += len(starts)
        self.start_events = collections.defaultdict(list)
        self.end_events = collections.defaultdict(list)


# Used to measure the time taken for multiple events
class Timer:
    def __init__(self, keys):
        self.keys = keys
        self.n = {}
        self.running_time = {}
        self.total_time = {}
        self.reset()

    def start(self, key):
        self.running_time[key] = time.time()
        return self

    def stop(self, key):
        self.total_time[key] = time.time() - self.running_time[key]
        self.n[key] += 1
        self.running_time[key] = None
        return self

    def reset(self):
        for k in self.keys:
            self.total_time[k] = 0
            self.running_time[k] = None
            self.n[k] = 0
        return self

    def value(self):
        vals = {}
        for k in self.keys:
            if self.n[k] == 0:
                raise ValueError("Trying to divide by zero in TimeMeter")
            else:
                vals[k] = self.total_time[k] / self.n[k]
        return vals
